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Dr.Haya Mohammad Abdulaziz Alaskar

Associate Professor Department of Computer Sciences College of Computer Engineering & Sciences
  • Kharj
  • 011-588-8810
  • h.alaskar@psau.edu.sa
  • Curriculum Vitae
  • Publications

Publications

  • H Alaskar, AJ Hussain, W Khan, H Tawfik, P Trevorrow, P Liatsis, Z Sbaï (2020) data
  • science approach for reliable classification of neuro-degenerative diseases using gait ,
  • Journal of Reliable Intelligent Environments, 1-15
  • Khan, A Hussain, K Kuru, H Al-Askar, (2020)Pupil Localisation and Eye Centre
  • Estimation Using Machine Learning and Computer Vision, Sensor
  • Ahmed, Z., Hussain, A., Khan, W., Baker, T., Al-Askar, H., Lunn, J., Liatsis, P., Al-Jumeily,
  • D., Al-Shabandar, R., (2020). Lossy and Lossless Video Frame Compression: A Novel
  • Approach for the High-Temporal Video Data Analytics. Remote Sensing, ISI, Scopus.
  • M IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models
  • Khalaf, H Alaskar, AJ Hussain, T Baker, Z Maamar, R Buyya, P Liatsis,IEEE Access 8,
  • 70375-70386
  • A Abdullahi, K Bawazeer, S Alotaibai, E Almoaither, M Al-Otaibi, H, alaskar,Pretrained
  • Convolutional Neural Networks for Cancer Genome Classification,3rd International
  • Conference on Computer Applications & Information
  • A Robust Quasi-Quantum Walks-Based Steganography Protocol for Secure Transmission
  • of Images on Cloud-Based E-healthcare Platforms
  • B Abd-El-Atty, AM Iliyasu, H Alaskar, A El-Latif, A Ahmed
  • Sensors 20 (11), 3108.
  • W Khan, A Hussain, H Alaskar, T Baker, F Ghali, D Al-Jumeily, 2020, Prediction of
  • Flood Severity Level Via Processing IoT Sensor Data Using Data Science Approach,
  • IEEE Internet of Things Magazine
  • H Alaskar, T Vaiyapuri, Z Sbai, 2019, Twitter Analytics for Discovering Socially
  • Important Locations for Business Improvement, IEEE International Symposium on Signal
  • Processing and Information …
  • Alaskar, H., (2019). High Predictive Performance of Dynamic Neural Network Models
  • for Forecasting Financial Time Series, ISI, Scopus.
  • Alaskar H., Alzhrani N., Hussain A., Almarshed F. (2019) The Implementation of
  • Pretrained AlexNet on PCG Classification. In: Huang DS., Huang ZK., Hussain A. (eds)
  • Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science,
  • vol 11645. Springer, Cham. ISI, Scopus.
  • Haya Alaskar, A. Hussain, Nourah Alaseem, Panos Liatsis, Dhiya Al-Jumeily:
  • Application of Convolutional Neural Networks for Automated Ulcer Detection in
  • Wireless Capsule Endoscopy Images. Sensors 19(6): 1265 (2019). ISI, Scopus. IF: 3.031.
  • H.Alaskar, 2018 Deep Learning-Based Model Architecture for Time-Frequency Images
  • Analysis, International Journal of Advanced Computer Science and Applications. ISI,
  • Scopus.
  • H. Alaskar, 2018, Deep Learning of EMG Time Frequency Representations for
  • Identifying Normal and Aggressive Actions, International Journal of Computer Science
  • and Network Security, Vol. 18 No. 12 pp. 16-25,
  • http://paper.ijcsns.org/07_book/201812/20181203.pdf. ISI
  • H. Alaskar, A. Hussain, 2018 ,Prediction of Parkinson Disease Using Gait Signals,
  • Eleventh International Conference on Developments in e-Systems Engineering, IEEE.ISI
  • Haya Alaskar , Convolutional Neural Network Application in Biomedical Signals, Journal
  • of Computer Science and Information Technology, American Research Institute, 2018,
  • Vol. 6, No. 2, pp. 45-59.
  • H. Tawfik, H. Alaskar, P. Liatsis, M. khalaf , 2018:A Dynamic Neural Network
  • Architecture with immunology Inspired Optimization for
  • Weather Data Forecasting , Big Data Research. ISSN 2214-5796 . ISI, Scopus.
  • IF:2.952
  • H. Alaskar, A. Hussain, Data Mining to Support the Discrimination of Amyo-trophic
  • lateral sclerosis Diseases Based on Gait Analysis In: Huang DS., Gromiha M., Han K.,
  • Hussain A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in
  • Computer Science, vol 10956. Online ISBN 978-3-319-95957-3, DOI
  • https://doi.org/10.1007/978-3-319-95957-3_80, ISI, Scopus.
  • H. Alasker , S. Alharkan, W. Alharkan ; A. Zaki ; L. Septem Riza,
  • 2017,Detection of kidney disease using various intelligent classifiers, Science in
  • Information Technology (ICSITech), 2017 3rd International Conference . IEEE
  • explore.
  • H. Alasker , A. Zaki , 2017Early Prediction of Chronic Kidney Disease Using Multiple
  • Automated Techniques, International Journal of Computing & Information Sciences
  • M. Khalaf, D. Al-Jumeily, R. Keight, R. Keenan, P. Fergus, H. Al-Askar, A. Shaw, I.
  • Idowu :Training Neural Networks as Experimental Models: Classifying Biomedical
  • Datasets for Sickle Cell, In: Huang DS., Bevilacqua V., Premaratne P. (eds) Intelligent
  • Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science,
  • vol 9771. Springer, Cham, Online ISBN 978-3-319-42291-6. DOI
  • https://doi.org/10.1007/978-3-319-42291-6_78 , ISI, Scopus. IF : 0.402
  • C. Montañez, P. Fergus, D. Al-Jumeily, B. Abdulaimma, H. Al-Askar :A Genetic
  • Analytics Approach for Risk Variant Identification to Support Intervention Strategies for
  • People Susceptible to Polygenic Obesity and Overweight. In: Huang DS., Bevilacqua V.,
  • Premaratne P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture
  • Notes in Computer Science, vol 9771. Springer, Cham, Online ISBN 978-3-319-42291-6 ,
  • DOI https://doi.org/10.1007/978-3-319-42291-6_80, ISI, Scopus.IF : 0.402
  • Al Kafri, S. Sudirman, P. Fergus, D. Al-Jumeily, M. Al-Jumaily, H. Al-Askar, A
  • Framework on a Computer Assisted and Systematic Methodology for Detection of
  • Chronic Lower Back Pain Using Artificial Intelligence and Computer Graphics
  • Technologies. . ICIC 2016. Lecture Notes in Computer Science, Springer, Cham. ISI,
  • Scopus. IF : 0.402
  • A.Hussain, D.Al-Jumeily, H. Al-Askar, N. Radi:
  • Regularized dynamic self-organized neural network inspired by the immune
  • algorithm for financial time series prediction. Neurocomputing 188: 23-30 (2016).
  • ISI, Scopus. IF: 3.317.
  • H. Alaskar, D. J. Lamb, A. Hussain, D. Al-Jumeily, M. Randles, P. Fergus:
  • Predicting financial time series data using artificial immune system-inspired neural
  • networks. IJAISC 5(1): 45-68 (2015)
  • A. Hussain, P. Fergus, H. Al-Askar, D. Al-Jumeily, F. Jager:
  • Dynamic neural network architecture inspired by the immune algorithm to predict
  • preterm deliveries in pregnant women. Neurocomputing 151: 963-974 (2015). ISI,
  • Scopus. IF: 3.317.
  • Reid D., Tawfik H., Hussain A.J., Al-Askar H. (2015) Forecasting Weather Signals Using
  • a Polychronous Spiking Neural Network. In: Huang DS., Bevilacqua V., Premaratne P.
  • (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in
  • Computer Science, vol 9225. Springer, Cham
  • A. Hussain, P. Fergus, D. Al-Jumeily, H. Alaskar, N. Radi:
  • The Utilisation of Dynamic Neural Networks for Medical Data Classifications- Survey
  • with Case Study. ICIC (3) 2015: 752-758
  • A. Hussain, D. Al-Jumeily, H. Al-Askar “The Application of Dynamic Self-organised
  • Multilayer network Inspired by the Immune Algorithm for weather signals forecast”, The
  • Third International Conference on Technological Advances in Electrical, Electronics and
  • Computer Engineering, TAEECE 2015, Beirut, Lebanon, 2015.
  • I. Idowu, Paul Fergus, A. Hussain, Chelsea Dobbins, H. Al-Askar:
  • Advance Artificial Neural Network Classification Techniques Using EHG for Detecting
  • Preterm Births. CISIS 2014, IEEE explore, 95-100
  • Hussain A.J., Al-Askar H., Al-Jumeily D. (2014) Physical Time Series Prediction Using
  • Dynamic Neural Network Inspired by the Immune Algorithm. In: Bouchachia A. (eds)
  • Adaptive and Intelligent Systems. ICAIS 2014. Lecture Notes in Computer Science, vol
  • 8779. Springer, Cham
  • Al-Askar H., Hussain A.J., Al-Jumeily D., Radi N. (2014) Regularized Dynamic Self
  • Organized Neural Network Inspired by the Immune Algorithm for Financial Time Series
  • Prediction. In: Huang DS., Han K., Gromiha M. (eds) Intelligent Computing in
  • Bioinformatics. ICIC 2014. Lecture Notes in Computer Science, vol 8590. Springer,
  • Cham
  • Alaskar H., Hussain A.J., Paul F.H., Al-Jumeily D., Tawfik H., Hamdan H. (2014)
  • Feature Analysis of Uterine Electrohystography Signal Using Dynamic Self-organised
  • Multilayer Network Inspired by the Immune Algorithm. In: Huang DS., Bevilacqua V.,
  • Premaratne P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in
  • Computer Science, vol 8588. Springer, Cha
  • Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction
  • Techniques for Detecting Preterm Births Using EHG Records. In: Huang DS., Han K.,
  • Gromiha M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in
  • Computer Science, vol 8590. Springer, Cham
  • Huang R., Tawfik H., Hussain A.J., Al-Askar H. (2014) The Application of Artificial
  • Immune Systems for the Prediction of Premature Delivery. In: Huang DS., Jo KH., Wang
  • L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer
  • Science, vol 8589. Springer, Cham
  • D. Al-Jumeily ; A. Hussain ; H. Alaskar (2013) Recurrent neural networks inspired by
  • artificial Immune algorithm for time series prediction. The 2013 International Joint
  • Conference on Neural Networks (IJCNN) Dallas, TX, USA: 1-8.
  • Invited Book Chapters:
  • “Recurrent Neural Networks in Medical Data Analysis and Classifications”,
  • in: D. Al-Jumeily, A. Hussain, C. Mallucci, C. Oliver (eds), Applied Computing
  • in Medicine and Health, Elsevier, 2015.

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